This four-day hands-on training course delivers the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system, and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.
This course is designed for developers and engineers who have programming experience, but prior knowledge of Spark and Hadoop is not required. Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.
- How the Apache Hadoop ecosystem fits in with the data processing lifecycle
- How data is distributed, stored, and processed in a Hadoop cluster
- How to write, configure, and deploy Apache Spark applications on a Hadoop cluster
- How to use the Spark shell and Spark applications to explore, process, and analyze distributed data
- How to query data using Spark SQL, DataFrames, and Datasets
- How to use Spark Streaming to process a live data stream